Search Results for author:
Found 6 papers, 1 papers with code
Deformable image registration with deep network priors: a study on longitudinal PET images
Inspired by Deep Image Prior, this paper introduces a different use of deep architectures as regularizers to tackle the image registration question.
Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge
Precise characterization of the kidney and kidney tumor characteristics is of outmost importance in the context of kidney cancer treatment, especially for nephron sparing surgery which requires a precise localization of the tissues to be removed.
Unsupervised Data Selection for Supervised Learning
Recent research put a big effort in the development of deep learning architectures and optimizers obtaining impressive results in areas ranging from vision to language processing.
Training of a Skull-Stripping Neural Network with efficient data augmentation
Skull-stripping methods aim to remove the non-brain tissue from acquisition of brain scans in magnetic resonance (MR) imaging.
Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging
Cluster of microcalcifications can be an early sign of breast cancer.
Synthetic contrast enhancement in cardiac CT with Deep Learning
In Europe the 20% of the CT scans cover the thoracic region.
